ZipRecruiter

Log In

1

Model Predictive Control Jobs in Baltimore, MD (NOW HIRING)

Everwatch

Systems Engineer, Senior

Annapolis Junction, MD · On-site

$106K - $146K/yr

Harnessing the most advanced technology and solutions, we strengthen defenses and control ... modeling, predictive analytics, or decision-support systems * Knowledge of data science, machine ...

Everwatch

Systems Engineer, Senior

Annapolis Junction, MD

$106K - $146K/yr

Harnessing the most advanced technology and solutions, we strengthen defenses and control ... modeling, predictive analytics, or decision-support systems * Knowledge of data science, machine ...

Everwatch

Systems Engineer, Senior

Annapolis Junction, MD

$106K - $146K/yr

Harnessing the most advanced technology and solutions, we strengthen defenses and control ... modeling, predictive analytics, or decision-support systems * Knowledge of data science, machine ...

Everwatch

Systems Engineer, Senior

Annapolis Junction, MD · On-site

$106K - $146K/yr

Harnessing the most advanced technology and solutions, we strengthen defenses and control ... modeling, predictive analytics, or decision-support systems * Knowledge of data science, machine ...

IBM

Sr. Software Engineer (TS/SCI)

Annapolis Junction, MD · On-site

$117K - $140K/yr

Responsibilities : • Develop Predictive Models: Design and implement predictive models using ... Stay up-to-date with industry-leading tools and technologies, including version control systems ...

next page

Showing results 1-20

All JobsModel Predictive Control JobsModel Predictive Control Jobs in Baltimore, MD

Model Predictive Control information

See Baltimore, MD salary details

$54.6K

$96K

$130.2K

How much do model predictive control jobs pay per year?

As of Jul 7, 2026, the average yearly pay for model predictive control in Baltimore, MD is $95,959.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $107,300.00 per year, depending on experience, location, and employer.

What is Model Predictive Control?

Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.

What is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, robotics, embedded systems, industrial sectors

Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.

What are the typical challenges faced by engineers working with Model Predictive Control (MPC) systems in an industrial setting?

Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.

What are the key skills and qualifications needed to thrive as a Model Predictive Control (MPC) Engineer, and why are they important?

To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.
What are popular job titles related to Model Predictive Control jobs in Baltimore, MD? For Model Predictive Control jobs in Baltimore, MD, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Baltimore, MD look for? The top searched job categories for Model Predictive Control jobs in Baltimore, MD are:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Baltimore, MD as of July 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 100% In-person job distribution, with an average salary of $95,959 per year, or $46.1 per hour.
Senior Guidance and Control Engineer

Senior Guidance and Control Engineer

Johns Hopkins Applied Physics Laboratory

Laurel, MD • On-site

$103K - $142K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted yesterday

Johns Hopkins Applied Physics Laboratory rating

9.9

Company rating: 9.9 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

1st of 58 rated research

Job description

Description
Are you passionate about collaborating with experienced engineers to design and develop the next generation of weapon systems?
Can you design and deliver cutting-edge guidance and control solutions, and guide their successful transition into operational weapon systems for our military?
If so, we're looking for someone like you to join our team at APL.
We are seeking an experienced Guidance & Control (G&C) engineer to deliver world-class analysis and development across a range of advanced vehicle systems. Emerging domains like hypersonic flight introduce complex design challenges, including rapidly changing nonlinear dynamics, unique control mechanisms, and mission-specific guidance in highly contested environments.
Innovations in areas like Artificial Intelligence & Machine Learning (AI/ML) and cooperative control for multi-agent systems also offer exciting opportunities to shape the future of our nation's defensive capabilities.
Our team supports a wide array of platforms, including hypersonics, cruise missiles, submarines, and autonomous flight vehicles. As a senior engineer, you will spearhead development of agile and robust guidance and control solutions for these critical systems, leveraging your deep technical expertise and passion for continuous learning.
As a Senior Guidance & Control Engineer, you will:
  • Apply your expertise in guidance and control to tackle complex technical challenges across a wide range of advanced autonomous vehicle systems.
  • Lead the research, design, and development of cutting-edge guidance and control algorithms, grounded in modern control theory and best practices.
  • Mentor and guide other GNC engineers and help ensure that our team maintains deep knowledge in the analysis, design, and implementation of control solutions for next-generation weapon systems.
  • Analyze and assess contractor designs to assist program offices throughout the DoD acquisition cycle and interface with contractor community to help them design and build operational weapon systems.
  • Collaborate closely with experts across disciplines to identify technical risks, communicate challenges to program sponsors, and help shape the strategic direction of your programs.

Qualifications
You meet our minimum qualifications for the job if you...
  • Possess a Bachelor's degree in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Mathematics, or another related technical field.
  • Have at least five years of experience in vehicle guidance, controls, or autonomous systems.
  • Are comfortable in building physics-based models, applying system dynamics and equations of motion in a simulation environment.
  • Are proficient in one or more areas of controls theory, including: classical methods (e.g. PID) and modern methods (e.g. nonlinear Lyapunov based, adaptive, robust, cooperative, and model predictive control) and their application to vehicle control design.
  • Have a strong sense of initiative, creativity, and a passion for learning.
  • Work effectively in a team and would like to mentor and lead technical staff members.
  • Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You'll go above and beyond our minimum requirements if you...
  • Possess a Master's degree or PhD in a related technical field.
  • Are proficient in guidance theory and application to flight systems (e.g. ProNav, GENEX, Optimal Guidance Methods)
  • Have deep knowledge in an affiliated field such as state estimation, navigation, optimization, system identification, hardware in the loop (HWIL) testing, or flight-testing missile systems.
  • Have significant experience in Artificial Intelligence & Machine Learning (AI/ML), especially as applied towards vehicle autonomy.
  • Have experience in project management, mentorship, and/or leading teams of people.

About Us
Why Work at APL?
The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.
At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at https://www.jhuapl.edu/careers.
All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accessibility@jhuapl.edu.
The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.
Minimum Rate
$100,000 Annually
Maximum Rate
$245,000 Annually

What Johns Hopkins Applied Physics Laboratory employees say

Pay

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply